Kidney cancer (also known as renal cell carcinoma [RCC]) is diagnosed in 36,000 patients and is the cause of death of 11-13,000 individuals yearly in the US; unfortunately (and for unknown reasons), the incidence of this disease is increasing in all groups. One-third of cases, many of whom are asymptomatic, are metastatic at diagnosis and there is currently no available biofluid diagnostic test or adequate treatment once diagnosed. Given the relationship of the kidney to the urine, RCC is ideally suited for identification of urinary markers. In this revised proposal, we will exploit the new science of metabolomics to discover a pattern of urinary metabolites which serve as biomarkers for RCC in patients who are at high risk for this disease. We will support our biomarkers discovery by using pathway and network analysis to confirm which metabolic pathways go awry in this disease, using RCC tissues and cell lines. Finally, we will test our biomarkers in new samples from both RCC non-RCC patients, including as controls patients with non-malignant renal disease as well as patients who have non-renal cancers. For this revision, we have improved the proposal by adding all of the requested preliminary data. We have also submitted two publications related to RCC metabolomics as well as proteomics. Our proposal is extraordinary in that we have assembled a unique cadre of collaborators: a cell biologist who is also a clinician- scientist nephrologist (Dr. Weiss), a proteomics and genomics expert (Dr. Perroud), four metabolomics experts (Drs Fiehn, Hammock, Michelmore, and Grant), two biostatisticians (Drs. Kim and Rocke), two oncologic pathologists (Drs. Grizzle and Borowsky), and two urologic oncologists (Drs. De Vere White and Evans) to utilize metabolomics to tackle the problem of diagnosis and treatment of a cancer which is difficult to diagnose, whose incidence is increasing, and for which current treatment options are dismal. We are pleased that the reviewers agreed that the experiments in our proposal were sound. Successful completion of these experiments will result in a major advance in diagnosis as well as, ultimately, the selection of optimal treatment regimens for this disease. Ours will be the first described use of this technology in urologic malignancy, and one of the first to exploit this technology in any cancer. Furthermore, our work can serve as a model for using metabolomics to glean oncogenic pathway and network data from a variety of cancers.